Over the past two or three decades, machine vision has been used increasingly and plays an important role in the design of automated manufacturing systems. A large variety of products, such as printed circuit boards (PCBs), integrated circuits, liquid crystal displays (LCDs), transistors, automotive parts, agricultural machines, and other products that are made in factories may need to be inspected during the production process. An improperly manufactured component may cause extensive damage to, render entirely or at least partially useless, ineffective, or at least not-fully functional, or otherwise impair a system containing the improperly manufactured component. Therefore, there is a need to ensure that all components are properly manufactured before they are used due to the high cost associated with functional failure. Machine vision systems have been used for quality control of products such as by identifying defects of the products, such as missing components, skewed components, reversed components, incorrectly placed components, or wrong valued components. Variance of placement and rotation of an object can result in position error and/or distortion error and negatively impact detection and accuracy. And variance of different objects on the same production line can negatively impact detection and accuracy. Rapid detection and analysis of an object and to quickly assess correct assembly of the object is desirable. Accordingly, there is a need for improved machine vision systems.